{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 14248,
     "status": "ok",
     "timestamp": 1586661386280,
     "user": {
      "displayName": "Xue Jiang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjfXAo9TmWFYS-GhDq8HOVk3N1FnJiyBPH8h9M=s64",
      "userId": "12544639825510830792"
     },
     "user_tz": -480
    },
    "id": "lF7F2fF4l9a6",
    "outputId": "f8fdbffd-8b22-4a6b-8ec3-4a1d281d538f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tesla T4'"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.cuda.get_device_name(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 559
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 21064,
     "status": "ok",
     "timestamp": 1586661393109,
     "user": {
      "displayName": "Xue Jiang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjfXAo9TmWFYS-GhDq8HOVk3N1FnJiyBPH8h9M=s64",
      "userId": "12544639825510830792"
     },
     "user_tz": -480
    },
    "id": "gPCIcggZnOC1",
    "outputId": "9e3d7beb-3dd2-4eeb-fd8b-909d4788d28e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting transformers\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a3/78/92cedda05552398352ed9784908b834ee32a0bd071a9b32de287327370b7/transformers-2.8.0-py3-none-any.whl (563kB)\n",
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      "Collecting tokenizers==0.5.2\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/3f/73c881ea4723e43c1e9acf317cf407fab3a278daab3a69c98dcac511c04f/tokenizers-0.5.2-cp36-cp36m-manylinux1_x86_64.whl (3.7MB)\n",
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      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
      "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.16.0,>=1.15.38->boto3->transformers) (0.15.2)\n",
      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.6/dist-packages (from botocore<1.16.0,>=1.15.38->boto3->transformers) (2.8.1)\n",
      "Installing collected packages: tokenizers, transformers\n",
      "Successfully installed tokenizers-0.5.2 transformers-2.8.0\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303,
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 42183,
     "status": "ok",
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     "user": {
      "displayName": "Xue Jiang",
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      "userId": "12544639825510830792"
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     "user_tz": -480
    },
    "id": "0tUrmlrHmaJB",
    "outputId": "0335d65d-61d9-496d-fa03-4d85de556725"
   },
   "outputs": [
    {
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    {
     "name": "stdout",
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     "text": [
      "\n"
     ]
    },
    {
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       "HBox(children=(IntProgress(value=0, description='Downloading', max=231508, style=ProgressStyle(description_wid…"
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "metadata": {
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ccb7a0d64cdf4c0fa6f0050c084c733c",
       "version_major": 2,
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       "HBox(children=(IntProgress(value=0, description='Downloading', max=267844284, style=ProgressStyle(description_…"
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     "metadata": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[{'label': 'NEGATIVE', 'score': 0.9991129}]\n",
      "[{'label': 'POSITIVE', 'score': 0.99986565}]\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "# 情感分类\n",
    "nlp = pipeline(\"sentiment-analysis\")\n",
    "\n",
    "print(nlp(\"I hate you\"))\n",
    "print(nlp(\"I love you\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h1RaH-2gdIh3"
   },
   "source": [
    "### 下面是一个使用模型进行序列分类的示例，以确定两个序列是否是彼此的解释。该过程如下：\n",
    "\n",
    "从checkpoint名称实例化一个tokenizer和一个模型。该模型被识别为一个BERT模型，并用存储在checkpoint中的权重加载它。\n",
    "\n",
    "从这两句话中构建一个序列，使用正确的特定于模型的分隔符标记类型id和注意力掩码(encode()和encode_plus()处理这个问题)\n",
    "\n",
    "将这个序列传递到模型中，以便将其分类到两个可用的类中的一个：0(不是解释)和1(是解释)\n",
    "\n",
    "计算结果的softmax获取类的概率\n",
    "\n",
    "打印结果"
   ]
  },
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      "\n",
      "{'input_ids': tensor([[  101,  1109,  1419, 20164, 10932,  2271,  7954,  1110,  1359,  1107,\n",
      "          1203,  1365,  1392,   102, 20164, 10932,  2271,  7954,   112,   188,\n",
      "          3834,  1132,  3629,  1107,  6545,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1]])}\n",
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     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased-finetuned-mrpc\")\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased-finetuned-mrpc\")\n",
    "\n",
    "classes = [\"not paraphrase\", \"is paraphrase\"]\n",
    "\n",
    "sequence_0 = \"The company HuggingFace is based in New York City\"\n",
    "sequence_1 = \"Apples are especially bad for your health\"\n",
    "sequence_2 = \"HuggingFace's headquarters are situated in Manhattan\"\n",
    "\n",
    "paraphrase = tokenizer.encode_plus(sequence_0, sequence_2, return_tensors=\"pt\")\n",
    "not_paraphrase = tokenizer.encode_plus(sequence_0, sequence_1, return_tensors=\"pt\")\n",
    "\n",
    "print(paraphrase)\n",
    "print(not_paraphrase)"
   ]
  },
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      "(tensor([[-0.3495,  1.9004]], grad_fn=<AddmmBackward>),)\n",
      "tensor([[-0.3495,  1.9004]], grad_fn=<AddmmBackward>)\n",
      "tensor([[ 0.5386, -2.2197]], grad_fn=<AddmmBackward>)\n"
     ]
    }
   ],
   "source": [
    "paraphrase_classification_logits = model(**paraphrase)[0]\n",
    "not_paraphrase_classification_logits = model(**not_paraphrase)[0]\n",
    "\n",
    "print(model(**paraphrase))\n",
    "print(paraphrase_classification_logits)\n",
    "print(not_paraphrase_classification_logits)"
   ]
  },
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     "text": [
      "tensor([[0.0954, 0.9046]], grad_fn=<SoftmaxBackward>)\n",
      "[[0.0953628346323967, 0.9046371579170227]]\n",
      "[0.0953628346323967, 0.9046371579170227]\n",
      "[0.94038325548172, 0.059616751968860626]\n"
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   ],
   "source": [
    "paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]\n",
    "not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]\n",
    "\n",
    "print(torch.softmax(paraphrase_classification_logits, dim=1))\n",
    "print(torch.softmax(paraphrase_classification_logits, dim=1).tolist())\n",
    "print(paraphrase_results)\n",
    "print(not_paraphrase_results)"
   ]
  },
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     "text": [
      "Should be paraphrase\n",
      "not paraphrase: 10%\n",
      "is paraphrase: 90%\n",
      "\n",
      "Should not be paraphrase\n",
      "not paraphrase: 94%\n",
      "is paraphrase: 6%\n"
     ]
    }
   ],
   "source": [
    "print(\"Should be paraphrase\")\n",
    "for i in range(len(classes)):\n",
    "    print(f\"{classes[i]}: {round(paraphrase_results[i] * 100)}%\")\n",
    "\n",
    "print(\"\\nShould not be paraphrase\")\n",
    "for i in range(len(classes)):\n",
    "    print(f\"{classes[i]}: {round(not_paraphrase_results[i] * 100)}%\")"
   ]
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